主成分分析
独立成分分析
模式识别(心理学)
残余物
人工智能
投影(关系代数)
职位(财务)
计算机科学
高斯过程
混合模型
投影寻踪
统计模型
数学
高斯分布
计算机视觉
算法
物理
经济
量子力学
财务
作者
Chi-Chen Raxle Wang,Jenn-Jier James Lien
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2008-03-01
卷期号:9 (1): 83-96
被引量:114
标识
DOI:10.1109/tits.2007.908572
摘要
This paper develops a novel statistical approach for automatic vehicle detection based on local features that are located within three significant subregions of the image. In the detection process, each subregion is projected onto its associated eigenspace and independent basis space to generate a principal components analysis (PCA) weight vector and an independent component analysis (ICA) coefficient vector, respectively. A likelihood evaluation process is then performed based on the estimated joint probability of the projection weight vectors and the coefficient vectors of the subregions with position information. The use of subregion position information minimizes the risk of false acceptances, whereas the use of PCA to model the low-frequency components of the eigenspace and ICA to model the high-frequency components of the residual space improves the tolerance of the detection process toward variations in the illumination conditions and vehicle pose. The use of local features not only renders the system more robust toward partial occlusions but also reduces the computational overhead. The computational costs are further reduced by eliminating the requirement for an ICA residual image reconstruction process and by computing the likelihood probability using a weighted Gaussian mixture model, whose parameters and weights are iteratively estimated using an expectation-maximization algorithm.
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